1. National Conference on Recent Advances in Wireless Communication & Artificial Intelligence (RAWCAI-2014) Organized by Department of Electronics & Communication Engineering, CTAE, Udaipur Application of AI for Effective Fault Diagnosis of High Speed Machines Presented by Dr.D.H.Pandya LDRP Institute of Technology & Research Gandhinagar,Gujarat

2. Experimental Setup 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat Spectrum Analyzer CoCo 80 Speed Controller D.C.Motor Piezoelectric Sensors Bearings Laser Tachometer Spring Type Flexible Coupling

3. 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat 1.Foundation – concrete, of dimension 190cm x 100cm with 1 cm thick rubber isolation 1.The Base Plate – Aluminum plate of dimensions 180cm x 90cm x 1.5cm 1.Electric Motor – 220-230 V, 50 Hz, 9000 rpm 1.Ball Bearing – 6205 (Single row deep groove) 1.Shaft Diameter – 1 inch

4. Raw Time Domain Signal Outer Race Defect, 2000 RPM 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

5. 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat Defect Identification with FFT analysis

6. 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat Bearing Characteristic Frequencies

7. Calculated Characteristic Frequencies Speed (RPM) f i f cage f bpfi (IRD) f bpo (ORD) f bsf (BD) 100016.76.6490.259.839.3 1500259.9613589.658.9 200033.313.318012078.6 250041.716.622614998.2 30005019.9271179118 350058.323.2316209138 400066.726.6361239157 45007529.9406269177 500083.333.2451299196 550091.736.5496329216 600010039.8541359236 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

8. Fast Fourier Transform of Outer Race Defect, 2000 RPM ω 1 = 68 Hz (~2 x f i = 66.6 Hz) ω 2 = 122 Hz (~f bpo =120 Hz) ω 3 = 150 Hz (~ f i + f bpo = 153.3 Hz) ω 4 = 190 Hz (~ f bpo +2 x f i = 186.6 Hz ) 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

9. 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat Feature Extraction using Wavelet Packet Transform (WPT)

10. Why wavelet ? Manual inspection ~ tiring process Noise hides important information Only frequency domain, time information lost. It is not suitable for non-stationary signals. Offer simultaneous localization in frequency and time domain. Computationally very fast. Flexibility: Small wavelets for finer details and large wavelets for coarser details. Used to extract important features for classification. In FFT Wavelet Transform 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

11. Wavelet Packet Decomposition Features Calculated: 1.Kurtosis 2.Energy 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

12. Matlab Functions used for Feature Extraction wpt = wpdec(x,n,'rbio5.5') E1 = wenergy(wpt) cfs(:,i) = wpcoef(wpt,[n i-1]) ku1(i) = kurtosis(cfs(:,i),1) WAVELET DECOMPOSITION eNERGY Wavelet coeffecients kurtosis 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

13. WPT Coefficients at Level 5 Decomposition 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

14. Selection of Real Wavelet with WPT 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat Wavelet Type Maximum Energy to Shannon Entropy ratio Maximum Relative wavelet Energy Db 101.29100.2728 Db 440.20660.3061 Rbio5.54.70950.3296 Sym20.44770.1736 Coif50.38710.2694

15. Data Preparation So finally, Features are extracted in the following form: [E1v,….,E32v,Ku1v..…,Ku32v, E1h..….,E32h,Ku1h…….,Ku32h] =128 Features Where each value is with reference to corresponding feature of a healthy bearing. Each input vector consists of 128 Features For each speed, we have, 10 data sets. We took data for 10 speeds: 1000, 2000 to 6000 RPM So, in all, we have 100 input vectors for each type of bearing So now we have, a 400 x 128 (100 samples for each bearing) 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

16. Data Normalization Data under each feature was normalized in MATLAB throughout all the 440 samples, to minimize the chances of error during classification Values were kept between 0.1 to 0.9, instead of 0 and 1. 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

17. Classification of Bearings Following softwares were used for classification purpose: 1. MATLAB nprtool (based on ANN) 2. WEKA Multilayer Perceptron (based on ANN) SMO algorithm (based on SVM) 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

18. Classification using MATLAB The “nprtool” of MATLAB was used for the classification of Bearings It uses two-layered feed-forward network for pattern recognition 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

19. Network Structure No. of SamplesPercentage Training28070 % Validation4010 % Testing8020 % 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

20. Data Sets for nPr Tool Input Data Vector [E1v,….,E32v,Ku1v..…,Ku32v, E1h..….,E32h,Ku1h…….,Ku32h] =128 Features Target Data IndexClassVector Assigned 1Ball Defect[1 0 0 0] 2Inner Race Defect[0 1 0 0] 3Outer Race Defect[0 0 1 0] 4Healthy Bearing[0 0 0 1] 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

21. Classification using WEKA In WEKA, following functions were used for classification: 1. Multilayer Perceptron (based on ANN) 1. SMO (based on SVM) 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

22. Network Performance - MLP 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

23. Network Performance – SMO (SVM) 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

24. Feature Optimization with accuracy rate 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

25. Time performance 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

26. Feature-Time-Accuracy 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

27. Performance Improvement after feature selection 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

28. Result summary 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

29. 10/12/2018 Author: D. H. PANDYA 29 Fault Diagnosis with confusion Matrix The results on a test set in a multi-class prediction are displayed as a two dimensional confusion matrix using 10-fold cross validation. Each matrix element is shown the number of test examples for which the actual class is the row and the predicted class is the column.

30. 10/12/2018 Author: D. H. PANDYA 30 Fault Diagnosis Test 1: in this test set input vector from healthy bearing is consider and output class assigned as HB. Test 2: in this set input vector from bearing with combined defect is considering and output class assigned as HB. Test 1Test 2 HBORDIRDBDCDHBORDIRDBDCD 10000HB00001 00000ORD00000 00000IRD00000 00000BD00000 00000CD00000 [E1v,….,E32v,Ku1v..…,Ku32v, E1h..….,E32h,Ku1h…….,Ku32h,1] =Input Vector

31. Conclusion In this study, it was found that Rbio5.5 is the best real wavelet for bearing fault diagnosis using maximum energy to entropy ratio criteria. Feature selection done using Correlation Based Feature Selection Algorithm improved performance of classifiers by decreasing the computational time by 93.5% (ANN), and 12.5% (SVM). 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

32. Conclusion 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat From here we can infer that ANN is most sensitive to number of features when is comes to computational time taken. It had been verified again that SVM is not much sensitive to number of attributes to computational time taken. ANN with Multilayer perceptron with CFS criteria was found to be the best classifier with a classification accuracy of 99.75% and computational time of just 3.16 seconds.

33. Impact Factor 1.88 SPRINGER Publication International Journals 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

34. Impact Factor 2.45 Impact Factor 1.99 ElSEVIER Publication SAGE Publication 10/12/2018 Dr.D.H.Pandya, LDRP-ITR, Gandhinagar,Gujarat

35. Ball Defect Inner Race Defect Visualizing the effect of top two Features 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

36. Future Work Finding optimum number of features for best classification Classification of bearing with all the three defects combined Using ANFIS and SOM techniques for classification of Bearings Using complex Wavelets for feature extraction. 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

37. 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat

38. 10/12/2018 Dr.D.H.Pandya, LDRP-ITR,Gandhinagar,Gujarat ?